Introduction “igraph is a collection of network analysis tools with the emphasis on efficiency, portability and ease of use. igraph is open source and free. igraph can be programmed in R, Python and C/C++.”

This procedure will identify sub-networks from a given network, using three clustering algorithms implemented by igraph: fast greedy, walktrap, and edge betweenness. The sub-networks will be further analyzed by enrichment analysis for their functional annotation.

 

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1 Summary

The full network includes 467 nodes and 1496 edges (3.2 edges per nodes). For enrichment analysis, 466 nodes in the network can be mapped to one or more node sets, while there are totally 28850 unique nodes in all node sets. The background of the enrichment analysis has 21426 nodes. The maximum number of edges a node has is 53 and 118 (25.27%) nodes have only one edge.

Figure 1. Number of edges per nodes.

2 Clustering analysis with different algorithms

2.1 Fast greedy

Figure 2A. Clustering via the fast greedy modularity optimization algorithm.

Figure 2B. Individual clusters identified via the fast greedy modularity optimization algorithm.

Table 1. Enrichment analysis of network clusters identified by the fast greedy algorithm.

Cluster Size Core_Member Top_Set Top_OddsRatio Top_PValue Top_FDR
Cluster_1 93 TP53 h_all_33 21.50 0 1e-07
Cluster_2 44 RPS5; RPL19; RPS23; RPL15; RPL4; RPS7; RPL12; RPL10A 491332 216.23 0 0e+00
Cluster_3 40 HSP90AA1 1127556 647.85 0 0e+00
Cluster_4 119 FYN hsa05205 20.24 0 0e+00
Cluster_5 28 POLR1B; RPLP0 486555 123.41 0 0e+00
Cluster_6 68 POLR2A 486891 34.91 0 0e+00

2.2 Walktrap

Figure 3A. Clustering via the Walktrap community finding algorithm.

Figure 3B. Individual clusters identified via the Walktrap community finding algorithm.

Table 2. Enrichment analysis of network clusters identified by the walkgrap algorithm.

Cluster Size Core_Member Top_Set Top_OddsRatio Top_PValue Top_FDR
Cluster_1 76 FYN hsa04810 28.34 0 0.00e+00
Cluster_2 46 TP53; HSP90AA1 c2_cp_BIOCARTA_88 153.25 0 1.55e-05
Cluster_3 24 NOP2 486555 122.00 0 0.00e+00
Cluster_4 50 POLR2A 160950 73.82 0 0.00e+00
Cluster_5 42 RPS5; RPL19; POLR1B; RPS23; RPL15; RPL4; RPLP0; RPS7; RPL12; RPL10A 491332 202.29 0 0.00e+00

2.3 Edge betweenness

Figure 4A. Clustering via the edge betweenness algorithm.

Figure 4B. Individual clusters identified via the edge betweenness algorithm.

Table 3. Enrichment analysis of network clusters identified by the edge betweenness algorithm.

Cluster Size Core_Member Top_Set Top_OddsRatio Top_PValue Top_FDR
Cluster_1 40 PPARA 160977 68.83 0 0.00e+00
Cluster_2 32 HSP90AA1 1127556 855.48 0 0.00e+00
Cluster_3 71 RPS5; RPL19; POLR1B; RPS23; RPL15; RPL4; RPLP0; RPS7; RPL12; RPL10A 491332 61.94 0 0.00e+00
Cluster_4 70 TP53 493498 10.79 0 2.89e-05
Cluster_5 98 FYN 106034 23.18 0 0.00e+00
Cluster_6 69 POLR2A 486891 37.15 0 0.00e+00

3 Session information

## R version 3.3.3 (2017-03-06)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X Yosemite 10.10.5
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] DEGandMore_0.0.0.9000 snow_0.4-2            igraph_1.1.2         
##  [4] GenomicRanges_1.26.4  GenomeInfoDb_1.10.3   IRanges_2.8.2        
##  [7] S4Vectors_0.12.2      BiocGenerics_0.20.0   htmlwidgets_0.9      
## [10] DT_0.2                awsomics_0.0.0.9000   yaml_2.1.16          
## [13] rmarkdown_1.8         knitr_1.18            RoCA_0.0.0.9000      
## [16] RCurl_1.95-4.9        bitops_1.0-6          devtools_1.13.4      
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.14    XVector_0.14.1  magrittr_1.5    zlibbioc_1.20.0
##  [5] highr_0.6       stringr_1.2.0   tools_3.3.3     withr_2.1.1    
##  [9] htmltools_0.3.6 rprojroot_1.3-2 digest_0.6.13   memoise_1.1.0  
## [13] evaluate_0.10.1 stringi_1.1.6   backports_1.1.2 jsonlite_1.5   
## [17] pkgconfig_2.0.1

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